nep-ets New Economics Papers
on Econometric Time Series
Issue of 2013‒02‒16
five papers chosen by
Yong Yin
SUNY at Buffalo

  1. A survey of econometric methods for mixed-frequency data By Claudia Foroni; Massimiliano Marcellino
  2. A Fractionally Integrated Wishart Stochastic Volatility Model By Manabu Asai; Michael McAleer
  3. Common non-linearities in multiple series of stock market volatility By Heather M. Anderson; Farshid Vahid
  4. Semiparametric Profile Likelihood Estimation of Varying Coefficient Models with Nonstationary Regressors By Kunpeng Li; Degui Li; Zhongwen Lian; Cheng Hsiao
  5. Orthogonal Expansion of Lévy Process Functionals: Theory and Practice By Chaohua Dong; Jiti Gao

  1. By: Claudia Foroni (Norges Bank (Central Bank of Norway)); Massimiliano Marcellino (European University Institute, Bocconi University and CEPR)
    Abstract: The development of models for variables sampled at di¤erent frequencies has attracted substantial interest in the recent econometric literature. In this paper we provide an overview of the most common techniques, including bridge equations, MIxed DAta Sampling (MIDAS) models, mixed frequency VARs, and mixed frequency factor models. We also consider alternative techniques for handling the ragged edge of the data, due to asynchronous publication. Finally, we survey the main empirical applications based on alternative mixed frequency models
    Keywords: mixed-frequency data, mixed-frequency VAR, MIDAS, nowcasting, forecasting
    JEL: E37 C53
    Date: 2013–02–06
  2. By: Manabu Asai (Faculty of Economics Soka University, Japan and Wharton School University of Pennsylvania); Michael McAleer (Econometric Institute Erasmus School of Economics Erasmus University Rotterdam and Tinbergen Institute, The Netherlands and Institute of Economic Research Kyoto University, Japan and Department of Quantitative Economics Complutense University of Madrid, Spain)
    Abstract: There has recently been growing interest in modeling and estimating alternative continuous time multivariate stochastic volatility models. We propose a continuous time fractionally integrated Wishart stochastic volatility (FIWSV) process. We derive the conditional Laplace transform of the FIWSV model in order to obtain a closed form expression of moments. We conduct a two-step procedure, namely estimating the parameter of fractional integration via log-periodgram regression in the rst step, and estimating the remaining parameters via the generalized method of moments in the second step. Monte Carlo results for the procedure shows reasonable performances in nite samples. The empirical results for the bivariate data of the S&P 500 and FTSE 100 indexes show that the data favor the new FIWSV processes rather than one-factor and two-factor models of Wishart autoregressive processes for the covariance structure.
    Keywords: Diusion process; Multivariate stochastic volatility; Long memory; Fractional Brownian motion; Generalized Method of Moments.
    JEL: C32 C51 G13
    Date: 2013–02
  3. By: Heather M. Anderson; Farshid Vahid
    Abstract: Decreases in stock market returns often lead to higher increases in volatility than increases in returns of the same magnitude, and it is common to incorporate these so-called leverage effects in GARCH and stochastic volatility models. Recent research has also found it useful to account for leverage in models of realized volatility, as well as in models of the continuous and jump components of realized volatility. This paper explores the use of smooth transition autoregressive (STAR) models for capturing leverage effects in multiple series of the continuous components of realized volatility. We find that the leverage effect is well captured by a common nonlinear factor driven by returns, even though the persistence in each country’s volatility is country specific. A three country model that incorporates both country specific persistence and a common leverage effect offers slight forecast improvements for mid-range horizons, relative to other models that do not allow for the common nonlinearity.
    Keywords: Realized Volatility, Bipower Variation, Common Factors, Fore-casting, Leverage, Smooth Transition Models.
    Date: 2013
  4. By: Kunpeng Li; Degui Li; Zhongwen Lian; Cheng Hsiao
    Abstract: We study a partially linear varying coefficient model where the regressors are generated by the multivariate unit root I(1) processes. The influence of the explanatory vectors on the response variable satisfies the semiparametric partially linear structure with the nonlinear component being functional coefficients. The profile likelihood estimation methodology with the first-stage local polynomial smoothing is applied to estimate both the constant coefficients in the linear component and the functional coefficients in the nonlinear component. The asymptotic distribution theory for the proposed semiparametric estimators is established under some mild conditions, from which both the parametric and nonparametric estimators are shown to enjoy the well-known super-consistency property. Furthermore, a simulation study is conducted to investigate the finite sample performance of the developed methodology and results.
    Keywords: Functional coefficients, local polynomial fitting, profile likelihood, semiparametric estimation, unit root process.
    Date: 2013
  5. By: Chaohua Dong; Jiti Gao
    Abstract: In this paper, expansions of functionals of Lévy processes are established under some Hilbert spaces and their orthogonal bases. From practical standpoint, both time-homogeneous and time-inhomogeneous functionals of Lévy processes are considered. Several expansions and rates of convergence are established. In order to state asymptotic distributions for statistical estimators of unknown parameters involved in a general regression model, we develop a general asymptotic theory for partial sums of functionals of Lévy processes. The results show that these estimators of the unknown parameters in different situations converge to quite different random variables. In addition, the rates of convergence depend on various factors rather than just the sample size. Simulations and empirical study are provided to illustrate the theoretical results.
    Keywords: Asymptotic theory Expansion, Lévy Process, Nonstationary time series, Orthogonal Series,
    Date: 2013

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